New HD

I got a new hard drive, because I have too many baby pictures—3 TB, fits in my pocket. It made me search for that picture of that 5 Megabyte drive that IBM sold back in the ’50s.

 

https://www.google.com/search?q=ibm+disk+drive+1956&source=lnms&tbm=isch&sa=X

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To read: What makes an academic paper useful for health policy?

https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-015-0544-8

Highly Accessed Articles
EDITORIAL

What makes an academic paper useful for health policy?

Christopher J M Whitty

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Reusable Holdout

Cool paper, cool idea, ICYMI:

http://science.sciencemag.org/content/sci/349/6248/636.full.pdf

From: Mabry, Patricia L
Sent: Thursday, January 14, 2016 5:51 AM
Subject: [iuni_systems_sci-l] Article of interest: reusable holdout method

Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., & Roth, A. (2015). The reusable holdout: Preserving validity in adaptive data analysis.Science, 349(6248), 636-638.

Misapplication of statistical data analysis is a common cause of spurious discoveries in
scientific research. Existing approaches to ensuring the validity of inferences drawn from data
assume a fixed procedure to be performed, selected before the data are examined. In common
practice, however, data analysis is an intrinsically adaptive process, with new analyses
generated on the basis of data exploration, as well as the results of previous analyses on the
same data. We demonstrate a new approach for addressing the challenges of adaptivity based
on insights from privacy-preserving data analysis. As an application, we show how to safely
reuse a holdout data set many times to validate the results of adaptively chosen analyses.

http://science.sciencemag.org/content/349/6248/636.full-text.pdf+html

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This might be better than dropping into the Python Debugger sometimes

http://stackoverflow.com/a/2158266/1935494

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New Publication: Implementing the PHMRC shortened questionnaire: Survey duration of open and closed questions in three sites

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178085

Abstract

Background

More countries are using verbal autopsy as a part of routine mortality surveillance. The length of time required to complete a verbal autopsy interview is a key logistical consideration for planning large-scale surveillance.

Methods

We use the PHMRC shortened questionnaire to conduct verbal autopsy interviews at three sites and collect data on the length of time required to complete the interview. This instrument uses a novel checklist of keywords to capture relevant information from the open response. The open response section is timed separately from the section consisting of closed questions.

Results

We found the median time to complete the entire interview was approximately 25 minutes and did not vary substantially by age-specific module. The median time for the open response section was approximately 4 minutes and 60% of interviewees mentioned at least one keyword within the open response section.

Conclusions

The length of time required to complete the interview was short enough for large-scale routine use. The open-response section did not add a substantial amount of time and provided useful information which can be used to increase the accuracy of the predictions of the cause of death. The novel checklist approach further reduces the burden of transcribing and translating a large amount of free text. This makes the PHMRC instrument ideal for national mortality surveillance.

Also with a replication archive on the Global Health Data Exchange (GHDx) [http://ghdx.healthdata.org/node/263527].

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A classroom-worthy example of the power of visual analytics

I started reading an “economics of diversity” book recently, and stumbled across a great example of the power of visual analytics (included early in the book to demonstrate the value of diverse representations):

This game is hard, right? I mean I have to think about it to figure out a good move. But if you think of it visually, the right way, it is not hard.  I’ll leave it as a mystery for now, and say that I can imagine a classroom exercise on this when I next teach interactive data visualization again.

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Testing what you don’t know

Did I mention that I attended a Software Carpentry (SWC) train-the-trainers event recently (editors note: not so recently anymore…)? And did I mention that they got me to read a fun book called _Teaching what you don’t know_? It had a number of fun-sounding ideas to encourage students to actively engage with material, in a chapter titled “Thinking in Class”, and I tried one out in a guest lecture.

The super-simple idea is this: at some point when students are spacing out from hearing too much talking from me, I paused for questions. When there were none, I said, “now I want you to turn to the person next to you, and spend just two minutes and see where your notes differ from theirs. And figure out what makes sense now but might not when you look back at your notes.”

Then I had a little break for two minutes, and people talked to each other. When I brought them back to me, there were questions and there was renewed attention.

I tried it again about 20 minutes later, and it didn’t have the same magic. Maybe it is a once a class thing.

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